A Modified Gash Model for Estimating Rainfall Interception Loss of Forest Using Remote Sensing Observations at Regional Scale

Cui, Yaokui; Li, Jia

doi:10.3390/w6040993

Cited by 48 publications

(47 citation statements)

References 30 publications

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“…

E_{I} = ({, \begin{matrix} F normalc \times P_{g, i} \\ Fc \times ({}, n \times P_{g}^{'} + \overset{E}{true} / \overset{R}{true} \times ((), P_{normalg, i} - P_{g}^{'})) \end{matrix}) \begin{matrix} true \\ P_{g, i} < P_{normalg}^{'} \\ P_{normalg} \geq P_{normalg}^{'} \end{matrix},

where Fc is the vegetation cover fraction; P g, i (millimetre) is the gross rainfall of the i th of n total rainfall events; Ē /

\overset{R}{true}

is the ratio of monthly averaged wet canopy evaporation rate ( Ē ; millimetre per hour) to the monthly averaged rainfall rate (

\overset{R}{true}

; millimetre per hour); and

P_{g}^{'}

is the threshold value of precipitation required to saturate the vegetation, which is estimated as

P_{g}^{'} = - S_{v} \times \log ((), 1 - \overset{E}{true} / \overset{R}{true}) / ((), \overset{E}{true} / \overset{R}{true}),

where S v is the vegetation capacity (millimetre) equal to the sum of the storage capacity of the canopy and that of the trunks.

S_{v} = S_{L} \times LAI + S_{T},

where LAI is the leaf area index; S L is canopy storage capacity per unit leaf area (millimetre) given according to the International Geosphere–Biosphere Programme vegetation types, and the values are adapted from the literature (van Dijk & Bruijnzeel, ; Cui et al, ); and S T is the storage capacity of branches, trunks, and dead leaves (millimetre), which can be estimated on the basis of the seasonal variation in LAI or its relationship with forest structure (Cui & Jia, ; van Dijk & Bruijnzeel,…”

Section: Methodsmentioning

confidence: 99%

Global canopy rainfall interception loss derived from satellite earth observations

Zheng

2020

Ecohydrology

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Information on global canopy rainfall interception loss is essential for understanding the dynamics of land surface processes and the water cycle. In addition, large uncertainty remains regarding the global variation in this factor. The development of satellite earth observations has provided a great opportunity for the estimation of global rainfall interception loss to reveal the spatiotemporal variations. In the current study, the analytical Gash model was adapted and applied to estimate global rainfall interception loss from 2001 to 2015 on the basis of satellite remote‐sensing products, for example, gross rainfall amount and rate and leaf area index. The Dalton‐type equation was adopted to estimate the wet canopy evaporation rate in the revised Gash model. The estimation on the basis of the Dalton‐type equation showed better agreement with the ground observations than the classical Penman–Monteith equation in terms of estimated rainfall interception loss and its ratio to gross rainfall. Large spatial variations in global rainfall interception loss were found, and high values appeared in the regions with dense vegetation cover and high gross rainfall amounts, for example, tropical rainforests, whereas high rainfall interception to gross rainfall ratios occurred in the regions with low rain rates and high vegetation cover. This study constitutes a significant step forward in understanding global hydrological cycle on the basis of earth observation products.

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“…

E_{I} = ({, \begin{matrix} F normalc \times P_{g, i} \\ Fc \times ({}, n \times P_{g}^{'} + \overset{E}{true} / \overset{R}{true} \times ((), P_{normalg, i} - P_{g}^{'})) \end{matrix}) \begin{matrix} true \\ P_{g, i} < P_{normalg}^{'} \\ P_{normalg} \geq P_{normalg}^{'} \end{matrix},

where Fc is the vegetation cover fraction; P g, i (millimetre) is the gross rainfall of the i th of n total rainfall events; Ē /

\overset{R}{true}

is the ratio of monthly averaged wet canopy evaporation rate ( Ē ; millimetre per hour) to the monthly averaged rainfall rate (

\overset{R}{true}

; millimetre per hour); and

P_{g}^{'}

is the threshold value of precipitation required to saturate the vegetation, which is estimated as

P_{g}^{'} = - S_{v} \times \log ((), 1 - \overset{E}{true} / \overset{R}{true}) / ((), \overset{E}{true} / \overset{R}{true}),

where S v is the vegetation capacity (millimetre) equal to the sum of the storage capacity of the canopy and that of the trunks.

S_{v} = S_{L} \times LAI + S_{T},

Section: Methodsmentioning

confidence: 99%

Global canopy rainfall interception loss derived from satellite earth observations

Zheng

2020

Ecohydrology

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“…In these models, LAI acts as the bridge to upscale the rate of leaf biophysical and biogeochemical processes, for example, leaf photosynthesis and stomatal conductance, to the canopy level (Mu et al, ; Niu et al, ; H. Yan et al, ). The canopy water storage capacity is calculated as a linear function of LAI (Bastiaanssen et al, ; Cui & Jia, ; van Dijk & Bruijnzeel, ). In a similar fashion, satellite‐derived LAI data are directly used to calculate the canopy conductance (Cleugh et al, ; Mu et al, ; H. Yan et al, ).…”

Section: Lai Applicationsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

558

252

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Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

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“…The interception of canopy and trunk was replaced by the interception of vegetation, and the sub-pixel heterogeneity was taken into account by applying a Poisson distribution function to the LAI value of each pixel. The detailed model description and validation is given in parallel work by Cui and Jia [50] and Cui et al [51].…”

Section: Interceptionmentioning

confidence: 99%

“…We estimated the interception of different vegetation types in the Heihe River basin using a reformulated Gash analytical model [48,49] called RS-Gash model (remote sensing based Gash model). The reformulated Gash model is forced by remote sensing observations of canopy structure (e.g., FVC, LAI, vegetation storage capacity) and rainfall intensity [50,51]. The interception of canopy and trunk was replaced by the interception of vegetation, and the sub-pixel heterogeneity was taken into account by applying a Poisson distribution function to the LAI value of each pixel.…”

Section: Interceptionmentioning

confidence: 99%

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

2015

Remote Sensing

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115

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Abstract:As a typical inland river basin, Heihe River basin has been experiencing severe water resource competition between different land cover types, especially in the middle stream and downstream areas. Terrestrial actual evapotranspiration (ETa), including evaporation from soil and water surfaces, evaporation of rainfall interception, transpiration of vegetation canopy and sublimation of snow and glaciers, is an important component of the water cycle in the Heihe River basin. We developed a hybrid remotely sensed ETa estimation model named ETMonitor to estimate the daily actual evapotranspiration of the Heihe River basin for the years 2009-2011 at a spatial resolution of 1 km. The model was forced by a variety of biophysical parameters derived from microwave and optical remote sensing observations. The estimated ETa was evaluated using eddy covariance (EC) flux observations at local scale and compared with the annual precipitation and the MODIS ETa product (MOD16) at regional scale. The spatial distribution and the seasonal variation of the estimated ETa were analyzed. The results indicate that the estimated ETa shows reasonable spatial and temporal patterns with respect to the diverse cold and arid landscapes in the upstream, middle stream and downstream regions, and is useful for various applications to improve the rational allocation of water resources in the Heihe River basin. OPEN ACCESSRemote Sens. 2015, 7 3057

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A Modified Gash Model for Estimating Rainfall Interception Loss of Forest Using Remote Sensing Observations at Regional Scale

Cited by 48 publications

References 30 publications

Global canopy rainfall interception loss derived from satellite earth observations

Global canopy rainfall interception loss derived from satellite earth observations

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations

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